Predicting Multiple ICD-10 Codes from Brazilian-Portuguese Clinical Notes

TL;DR

This paper explores automated ICD-10 coding from Brazilian Portuguese clinical notes using various neural network models, achieving competitive results and demonstrating the effectiveness of document concatenation.

Contribution

It introduces a new dataset of Brazilian Portuguese clinical notes and evaluates multiple neural network models, including CNN with Attention, for ICD code prediction.

Findings

CNN-Att achieves the highest F1 scores on both datasets.

Concatenating additional documents significantly improves performance.

Models outperform previous work on MIMIC-III dataset.

Abstract

ICD coding from electronic clinical records is a manual, time-consuming and expensive process. Code assignment is, however, an important task for billing purposes and database organization. While many works have studied the problem of automated ICD coding from free text using machine learning techniques, most use records in the English language, especially from the MIMIC-III public dataset. This work presents results for a dataset with Brazilian Portuguese clinical notes. We develop and optimize a Logistic Regression model, a Convolutional Neural Network (CNN), a Gated Recurrent Unit Neural Network and a CNN with Attention (CNN-Att) for prediction of diagnosis ICD codes. We also report our results for the MIMIC-III dataset, which outperform previous work among models of the same families, as well as the state of the art. Compared to MIMIC-III, the Brazilian Portuguese dataset contains far fewer words per document, when only discharge summaries are used. We experiment concatenating additional documents available in this dataset, achieving a great boost in performance. The CNN-Att model achieves the best results on both datasets, with micro-averaged F1 score of 0.537 on MIMIC-III and 0.485 on our dataset with additional documents.